Assessment and management of river water quality by deterministic modeling and ecological risk analysis: Aksu stream application

In recent years, the increase in demand for water resources for agricultural, industrial, and domestic purposes has led to adverse changes in both the quantity and quality of water. Intensive agricultural activities and urban development in the vicinity of rivers in our country contribute to the emergence of point and non-point source water pollution issues, posing significant risks to the continuity of aquatic ecological resources and the life cycles of aquatic organisms. In this context, numerous studies have been conducted on the water quality of Aksu Stream, which is a vital water source for Antalya. These investigations encompass the evaluation of physicochemical parameters to assess the current state of the stream's water quality. The aim of this thesis is to conduct a simulation using the WASP8 deterministic river water quality model to assess the transport of conventional pollutants from point and non-point sources in the area between the Karacaören 2 Dam and the Mediterranean Sea within the Aksu Stream Basin. Additionally, the study involves performing a risk analysis for pesticides and identifying and evaluating management practices that can be implemented for the improvement of both the quantity and quality of water through scenario analysis. Water quality monitoring activities were conducted monthly at 12 monitoring points over the course of one year, and pesticide analyses were performed every two months at 6 monitoring points for a total of 6 times during the same period. The monitoring study included physicochemical and bacteriological parameters, along with 167 different pesticides. Prominent results in terms of bacteriological parameters include a total coliform bacteria count of 13,600 colonies/100 mL, fecal coliform bacteria count of 1,460 colonies/100 mL, and Escherichia coli (E.coli) count of 162 colonies/100 mL at the Aksu 10 monitoring station where the stream discharges into the sea. The primary reason for these results is attributed to the discharge from the Kundu Tourism Investors Wastewater Treatment Plant (KUYAB WWTP), located in the downstream section of the stream. The MapShed hydrology-watershed model was employed for the modeling of non-point source pollution in the Aksu Stream Basin, yielding results related to diffuse loads. Additionally, the WASP8 water quality model was utilized for the modeling of Aksu Stream water quality and integrated into the MapShed model. In the deterministic model application, calibration and validation studies were initially conducted, resulting in successful predictions. For modelling based scenario analysis, nine distinct scenarios were developed, encompassing climate change, point and non-point pollution sources, and management practices for water withdrawals from the Aksu Stream for irrigation purposes. In comparison to the current state, the best practices for controlling non-point source pollution from agricultural areas were determined to be agricultural land-based non-point source pollution control scenarios (S4 and S5), as revealed by the comparison of scenarios related to pasture management and agricultural land management (S3, S4, and S5). Generally, scenarios S8 and S9, which focus on water withdrawals for agricultural irrigation purposes, have impacted the Aksu 5 station and the downstream section. This is primarily due to the extensive agricultural practices in the middle and lower basin. The effects on both water quality and quantity are evident in the downstream section (between stations Aksu 7 and Aksu 10) where increased water withdrawals in scenario S8 result in deteriorations in NH4-N, NO3-N, TN, Inorg-P, and TP concentrations. The primary pressures contributing to the deterioration of water quality include the addition of wastewater discharges beyond the Aksu 9 station, diffuse pollution during rainy periods, and a reduction in flow values during dry periods, leading to a decrease in waste assimilation/dilution capacity. Among the 167 pesticides included in the monitoring study, a total of 26 pesticides were detected at levels above the limit of detection (LOD). These pesticides comprise 5 from the priority pollutants list and 18 from the specific pollutants list presented in the Surface Water Quality Regulation. Examining the results of pesticide analysis, the highest detection count, with a value of 31, was observed at monitoring point Aksu 8, followed by 28 at Aksu 1, 26 at Aksu 5, 25 at Aksu 10, 23 at Aksu 4, and 22 at Aksu 9, respectively. The high frequency of pesticide occurrence in the middle and lower parts of the basin is closely related to the spatial distribution of agricultural lands in the basin. The most frequently detected pesticide active ingredients were Pendimethalin, Metolachlor, and Theta-cypermethrin. Environmental Impact Quotient (EIQ) and Pesticide Environmental Risk Indicator (PERI) assessment tools were employed for pesticide risk assessment studies. Using the EIQ model, environmental risk scores were obtained for 39 target herbicide, fungicide, and insecticide active ingredients, encompassing components for farmers, consumers, and ecology. Due to the absence of six pesticide active ingredients in the pesticide database, EIQ scores could not be calculated for these substances. Within the herbicide, fungicide, and insecticide groups, Oxadiazon, Spiroxamine, and Carbofuran pesticide active ingredients received the highest environmental risk scores, while Metazachlor, Fosetyl-Al, and Spinosad pesticide active ingredients obtained the lowest EIQ component scores. In the thesis study, ten pesticide active ingredients (Carbofuran, Thiamethoxam, Glyphosate-Isopropylamine, Mancozeb, Carboxin, Pendimethalin, Carbaryl, Fosetyl-Al, Oxadiazon, and Alachlor) were rated with high Environmental Impact Quotient (EIQ) Field scores. Results were normalized for the comparison of scores obtained from the EIQ and Pesticide Environmental Risk Indicator (PERI) pesticide risk assessment tools. According to the normalized EIQ scores, Carbofuran, DNOC, Fenthion, and Zeta-cypermethrin active components received the highest values. However, for the normalized PERI index, all scores, except for Trifluralin, remained within the range of 0.6 to 0.4. The thesis study presents an integrated approach encompassing water quality monitoring, deterministic modeling, and environmental risk analysis for pesticides to manage river water quality effectively.